Skip to main content

​
AI Router

​
Overview

Agno is a Python framework for building production-ready AI agents with structured workflows, tool integration, and multi-agent orchestration. By connecting Agno to Orq.ai’s AI Router, you get access to 300+ models for your agents with a single configuration change.

​
Key Benefits

Orq.ai’s AI Router enhances your Agno applications with:

Complete Observability

Track every agent interaction, tool use, and model call with detailed traces

Built-in Reliability

Automatic fallbacks, retries, and load balancing for production resilience

Cost Optimization

Real-time cost tracking and spend management across all your AI operations

Multi-Provider Access

Access 300+ LLMs and 20+ providers through a single, unified integration

​
Prerequisites

Before integrating Agno with Orq.ai, ensure you have:
  • An Orq.ai account and API Key
  • Python 3.10 or higher
To setup your API key, see API keys & Endpoints.

​
Installation

pip install agno

​
Configuration

Configure Agno to use Orq.ai’s AI Router via OpenAILike with a custom base_url:
Python
import os
from agno.models.openai.like import OpenAILike

llm = OpenAILike(
    id="openai/gpt-4o",
    api_key=os.getenv("ORQ_API_KEY"),
    base_url="https://api.orq.ai/v2/router",
)
base_url: https://api.orq.ai/v2/router

​
Basic Agent Example

Python
import os
from agno.agent import Agent
from agno.models.openai.like import OpenAILike

llm = OpenAILike(
    id="openai/gpt-4o",
    api_key=os.getenv("ORQ_API_KEY"),
    base_url="https://api.orq.ai/v2/router",
)

agent = Agent(
    model=llm,
    instructions="You are a helpful assistant. Keep responses concise.",
    markdown=True,
)

agent.print_response("Explain machine learning in two sentences.")

​
Agent with Tools

Add tools to your agent for real-world tasks:
Python
import os
from agno.agent import Agent
from agno.models.openai.like import OpenAILike
from agno.tools.yfinance import YFinanceTools

llm = OpenAILike(
    id="openai/gpt-4o",
    api_key=os.getenv("ORQ_API_KEY"),
    base_url="https://api.orq.ai/v2/router",
)

agent = Agent(
    model=llm,
    tools=[YFinanceTools(stock_price=True)],
    instructions="Use tables to display data. Don't include any other text.",
    markdown=True,
)

agent.print_response("What is the stock price of Apple?", stream=True)

​
Agent with Custom Tools

Define your own Python functions as tools:
Python
import os
from agno.agent import Agent
from agno.models.openai.like import OpenAILike

def get_weather(city: str) -> str:
    """Get the current weather for a city."""
    return f"Weather in {city}: Sunny, 22°C."

def convert_currency(amount: float, from_currency: str, to_currency: str) -> str:
    """Convert an amount between currencies."""
    return f"{amount} {from_currency} = {amount * 1.1:.2f} {to_currency} (approximate)"

llm = OpenAILike(
    id="openai/gpt-4o",
    api_key=os.getenv("ORQ_API_KEY"),
    base_url="https://api.orq.ai/v2/router",
)

agent = Agent(
    model=llm,
    tools=[get_weather, convert_currency],
    instructions="Use the available tools to answer questions accurately.",
    markdown=True,
)

agent.print_response("What's the weather in Tokyo and convert 100 USD to EUR?")

​
Multi-Agent Team

Orchestrate multiple specialized agents:
Python
import os
from agno.agent import Agent
from agno.models.openai.like import OpenAILike
from agno.team import Team

llm = OpenAILike(
    id="openai/gpt-4o",
    api_key=os.getenv("ORQ_API_KEY"),
    base_url="https://api.orq.ai/v2/router",
)

researcher = Agent(
    name="Researcher",
    model=llm,
    instructions="You research topics and provide accurate, concise facts.",
    markdown=True,
)

writer = Agent(
    name="Writer",
    model=llm,
    instructions="You take research and turn it into clear, engaging content.",
    markdown=True,
)

team = Team(
    members=[researcher, writer],
    model=llm,
    instructions="Collaborate to produce well-researched, well-written content.",
    markdown=True,
)

team.print_response("Write a short paragraph about renewable energy benefits.")

​
Model Selection

With Orq.ai, you can use any supported model from 20+ providers:
Python
import os
from agno.models.openai.like import OpenAILike

# Use Claude
claude_llm = OpenAILike(
    id="anthropic/claude-sonnet-4-5-20250929",
    api_key=os.getenv("ORQ_API_KEY"),
    base_url="https://api.orq.ai/v2/router",
)

# Use Gemini
gemini_llm = OpenAILike(
    id="google/gemini-2.5-flash",
    api_key=os.getenv("ORQ_API_KEY"),
    base_url="https://api.orq.ai/v2/router",
)

# Use Groq
groq_llm = OpenAILike(
    id="groq/llama-3.3-70b-versatile",
    api_key=os.getenv("ORQ_API_KEY"),
    base_url="https://api.orq.ai/v2/router",
)

​
Observability

​
Installation

pip install opentelemetry-api \
    opentelemetry-sdk \
    "opentelemetry-exporter-otlp-proto-http" \
    openinference-instrumentation-agno

​
Configuring Orq.ai Observability

Set up the tracer provider and instrument Agno before creating any agents: 
import os
from opentelemetry import trace as trace_api
from opentelemetry.sdk.trace import TracerProvider
from opentelemetry.sdk.trace.export import BatchSpanProcessor
from opentelemetry.exporter.otlp.proto.http.trace_exporter import OTLPSpanExporter

exporter = OTLPSpanExporter(
    endpoint="https://api.orq.ai/v2/otel/v1/traces",
    headers={"Authorization": f"Bearer {os.environ['ORQ_API_KEY']}"},
)
tracer_provider = TracerProvider()
tracer_provider.add_span_processor(BatchSpanProcessor(exporter))
trace_api.set_tracer_provider(tracer_provider)

from openinference.instrumentation.agno import AgnoInstrumentor
AgnoInstrumentor().instrument()
The OTEL setup and AgnoInstrumentor().instrument() call must happen before any agent instantiation.

​
Basic Example

import os
from opentelemetry import trace as trace_api
from opentelemetry.sdk.trace import TracerProvider
from opentelemetry.sdk.trace.export import BatchSpanProcessor
from opentelemetry.exporter.otlp.proto.http.trace_exporter import OTLPSpanExporter

exporter = OTLPSpanExporter(
    endpoint="https://api.orq.ai/v2/otel/v1/traces",
    headers={"Authorization": f"Bearer {os.environ['ORQ_API_KEY']}"},
)
tracer_provider = TracerProvider()
tracer_provider.add_span_processor(BatchSpanProcessor(exporter))
trace_api.set_tracer_provider(tracer_provider)

from openinference.instrumentation.agno import AgnoInstrumentor
AgnoInstrumentor().instrument()

from agno.agent import Agent
from agno.models.openai.like import OpenAILike

agent = Agent(
    name="orq-demo",
    model=OpenAILike(
        id="openai/gpt-4o-mini",
        api_key=os.getenv("ORQ_API_KEY"),
        base_url="https://api.orq.ai/v2/router",
    ),
    markdown=True,
)
agent.print_response("Summarize the latest AI news.")